





import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


snake_case_           =     datasets.logging.get_logger(__name__)


snake_case_           =     '\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

snake_case_           =     '\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

snake_case_           =     '\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

snake_case_           =     {
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		, _KWARGS_DESCRIPTION    )
class 						SCREAMING_SNAKE_CASE__							(			datasets.Metric    ):


      def   a					(self			: List[str] ):





         """simple docstring"""



         return datasets.MetricInfo(
             description=_DESCRIPTION							,     citation=_CITATION							,     homepage='''https://github.com/google-research/bleurt'''							,     inputs_description=_KWARGS_DESCRIPTION							,     features=datasets.Features(
                 {
                     '''predictions''': datasets.Value('''string'''							,     id='''sequence''' ),
                     '''references''': datasets.Value('''string'''							,     id='''sequence''' ),
                 } )							,     codebase_urls=['''https://github.com/google-research/bleurt''']							,     reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']							,     )


      def   a					(self			: Optional[Any]							,     a__			: Tuple ):





         """simple docstring"""



         if self.config_name == "default":
            logger.warning(
                '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
                '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').''' )
            __snake_case							=  '''bleurt-base-128'''

         if self.config_name.lower() in CHECKPOINT_URLS:
            __snake_case							=  self.config_name.lower()

         elif self.config_name.upper() in CHECKPOINT_URLS:
            __snake_case							=  self.config_name.upper()

         else:
            raise KeyError(
                f"""{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}""" )

         # download the model checkpoint specified by self.config_name and set up the scorer
         __snake_case							=  dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
         __snake_case							=  score.BleurtScorer(os.path.join(lowercase_							,     lowercase_ ) )


      def   a					(self			: Tuple							,     a__			: Optional[Any]							,     a__			: Dict ):





         """simple docstring"""



         __snake_case							=  self.scorer.score(references=lowercase_							,     candidates=lowercase_ )
         return {"scores": scores}


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__    ,     a__ )      ->int:




  '''simple docstring'''

  if index == number_of_items:
    return 0
  _UpperCamelCase           =    0
  _UpperCamelCase           =    0
  _UpperCamelCase           =    knapsack(a__    ,     a__    ,     a__    ,     a__    ,     index + 1 )
  if weights[index] <= max_weight:
    _UpperCamelCase           =    values[index] + knapsack(
        a__    ,     a__    ,     a__    ,     max_weight - weights[index]    ,     index + 1 )
  return max(a__    ,     a__ )


if __name__ == "__main__":
   import doctest

   doctest.testmod()




